Key Responsibilities:

Data Lake and Data Engineering:

- Design, develop and maintain scalable ETL/ELT pipelines using Snowflake, AWS Glue, SQL and Python.

- Build and maintain curated, reusable and governed data products for BI, analytics, Data Science and AI use cases.

- Contribute to the architecture and development of Snowflake-based cloud data warehouse and lakehouse solutions.

- Develop data models and optimize data storage, processing and retrieval performance in Snowflake.

- Ensure data accuracy, completeness, integrity and consistency across source and target systems.

- Monitor, troubleshoot and improve pipeline reliability, performance and data-loading SLAs.

- Implement appropriate logging, testing, documentation and operational support for data pipelines.

- Collaborate with source-system teams, analysts and business stakeholders to translate requirements into data solutions.

Data Science and AI Application Enablement:

- Prepare analysis-ready and feature-ready datasets for Data Science and AI applications.

- Collaborate with Data Scientists and AI developers to operationalize models and AI use cases.

- Support integration of Snowflake Cortex AI, AWS Bedrock, APIs and related AI services with enterprise data platforms.

- Build and maintain data pipelines required for model inference, AI agents and business applications.

- Apply security, access-control and data-governance standards to datasets consumed by AI applications.

Mandatory Experience and Skills:

- 3 to 5 years of overall experience, including at least 2 years of hands-on cloud data engineering experience.

- Minimum 2 years of hands-on Snowflake experience, covering data modelling, SQL development, performance optimization and ETL/ELT integration.

- Strong hands-on skills in advanced SQL and Python.

- Understanding of data security, RBAC, lineage and governance concepts.

- Hands-on experience with AWS Glue or a comparable cloud data-integration service.

- Good understanding of dimensional modelling, data warehousing and lakehouse architecture.

- Experience in data quality, pipeline monitoring, troubleshooting and performance optimization.

- Working knowledge of Git, CI/CD and Agile software-delivery practices.

- Strong analytical, problem-solving and stakeholder communication skills.

- Bachelor's degree in Computer Science, Information Technology or a related discipline.

Good to Have:

- Exposure to Snowflake Cortex AI, AWS Bedrock, Copilot Studio or other GenAI platforms.

- Experience supporting model deployment, inference pipelines, AI agents or AI-enabled applications.

- Exposure to Finance, Accounts, Sales, Supply Chain or Manufacturing data domains.

Role Positioning:

This is primarily a hands-on Snowflake Data Engineering role with responsibility for enabling Data Science and AI applications.